@article{CHEN2025, 
author = {Changkun CHEN and Yipeng BAO and Jian ZHANG and Rongfu YU},
title = {Motion state recognition method of rescue personnel based on triaxial motion data},
year = {2025},
journal = {Journal of Tsinghua University (Science and Technology)},
volume = {65},
number = {6},
pages = {1019-1026},
keywords = {triaxial motion data, rescue personnel, motion state recognition, time-domain feature},
url = {https://www.sciopen.com/article/10.16511/j.cnki.qhdxxb.2025.22.018},
doi = {10.16511/j.cnki.qhdxxb.2025.22.018},
abstract = {ObjectiveThe safety of rescue personnel is a critical factor in determining the success of rescue operations. The ability to accurately identify the motion states of rescue personnel is key to ensuring their safety. However, monitoring their motion states in real time is challenging because of the complex and dangerous environment they operate in. This study aims to develop a method for identifying the motion states of rescue personnel based on triaxial motion data to enhance the efficiency of personnel safety monitoring during rescue missions.MethodsIn this study, the MPU6050 sensor, an integrated triaxial accelerometer and gyroscope, was utilized to collect the motion data from the leg and waist of rescue personnel. This sensor was selected based on its low power consumption, automatic sleep mode, and power management features, making it suitable for long-duration rescue tasks. Before data collection, the sensors were calibrated using zero-bias calibration to reduce errors and ensure data reliability. These sensors were strategically placed on the waist the and leg of the rescue personnel to capture their overall body dynamics and detailed movements. This study analyzed the acceleration data under four different motion states: standing still, working in a small area, walking, and running. The data were analyzed using time-domain feature analysis, focusing on the standard deviation of acceleration to quantify the fluctuation and stability of the motion states. This study proposed a classification mechanism based on the sum of the standard deviations of waist and leg accelerations to distinguish between different motion states.ResultsThe experimental results demonstrated that the proposed method effectively distinguished between different motion states. In the standing-still state, the total acceleration was close to zero, indicating no movement. In the state of working in a small area, the acceleration was greater than zero but remained within a small range with stable fluctuations. In the walking state, there was a significant difference between the waist and leg total accelerations, with the latter showing larger fluctuations and clear peaks and valleys. In the running state, both waist and leg total accelerations showed larger fluctuations, with the latter having a greater amplitude. The method showed high accuracy and stability in real-time monitoring of rescue personnel's motion states, effectively identifying the changes in motion states within a 2-min test period. The standard deviation analysis revealed a clear hierarchical distribution, indicating significant differences in acceleration fluctuations between different motion states. The sum of the standard deviations of waist and leg accelerations provided a reliable basis for distinguishing between the four motion states.ConclusionsThis study has provided a reliable method for monitoring the motion states of rescue personnel, which can substantially improve the safety and efficiency of rescue operations. The method's ability to accurately and stably identify different motion states in real time makes it a valuable tool for ensuring the safety of rescue personnel in complex and dangerous environments. The findings of this study contribute to the development of more effective monitoring systems for rescue operations, potentially reducing the risk of accidents and enhancing the overall success rate of rescue missions.}
}